多传感器最优信息融合Kalman多步预报器及其应用

提出了一种新的标量加权线性最小方差意义下的多传感器最优信息融合算法。该算法考虑了局部估计误差之间的相关性，只需计算加权标量，避免了加权矩阵的计算，明显减小了计算负担，便于实时应用。基于该融合算法，对被多个传感器观测的离散线性随机系统，给出了具有容错性的多传感器标量加权最优信息融合分布式Kalman多步预报器。它具有两层融合结构，其中第一融合层具有网状并行结构，用来获得每时刻每两个无故障传感器子系统之间的滤波误差互协方差阵；第二融合层用来确定最优标量加权系数，进而获得标量加权最优融合Kalman多步预报器。将其应用于雷达跟踪系统验证了其有效性。

Multi-sensor optimal information fusion kalman multi-step predictor with application

A new multi-sensor optimal information fusion algorithm weighted by scalars is presented in the linear minimum variance sense.The algorithm considers the correlation of estimation errors among local subsystems,only requires the computation of the weighting scalars,and avoids the computation of the weighting matrices,so that the computational burden can obviously be reduced,and it is convenient to apply in real time.Based on this fusion algorithm,a scalar weighting multi-sensor optimal information fusion decentralized Kalman multi-step predictor with a fault tolerant property is given for the discrete linear stochastic system measured by multiple sensors.It has a two-layer fusion structure whose first fusion layer has a netted parallel structure to obtain the filtering error cross-covariance matrices between every two faultless sensor subsystems at each time step;the second fusion layer is used to determine the optimal scalar weighting coefficients,and obtain the optimal fusion Kalman multi-step predictor weighted by scalars.Applying it into a radar tracking system shows its effectiveness.